Titlebook: Computational Intelligence for Remote Sensing; Manuel Gra?a,Richard J. Duro Book 2008 Springer-Verlag Berlin Heidelberg 2008 Markov.Markov

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A Computation Reduced Technique to Primitive Feature Extraction for Image Information Mining Via thhives then in multimedia image archives. Hence, the adoption of new technologies that allow the accessibility of remote sensing data based on content and semantics is required to overcome this challenge and increase useful exploitation of the data [1, 2, 3].

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Automatic Preprocessing and Classification System for High Resolution Ultra and Hyperspectral Image spectral dimension. Regardless of the application of the images, the analysis methods employed must deal with large quantities of data efficiently [1]. Originally, imaging spectroradiometers were used as airborne or spaceborne remote sensing instruments. During the last decade, spectral imaging has

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Unsupervised Change Detection from Multichannel SAR Data by Markov Random Fields,y) SAR represents an option with improved potential: as compared with single-channel SAR, it is expected to provide an increased discrimination capability, while maintaining its insensitivity to atmospheric and illumination issues. This potential is also reinforced by the availability of multichanne

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Public Health Communication and Growthperposition of the radiation reflected in three broad band of the spectrum, typically blue, green and red bands. Much more information can be obtained if the photographs are taken in tens or hundreds of different spectral bands. Imaging spectrometers do this work. They take separated images at narro

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Albert Anani-Bossman,Isaac Abeku Blanksonent these conflicting criteria, and that can be efficiently evaluated. The application of an evolutionary algorithm (MOGA) to the optimal watermarking hyperspectral images is presented. Given an appropriate initialization, the algorithm can perform the search for the optimal mark placement in the or

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Sustainability and Communication, of some of the proposed techniques are also developed to satisfy time-critical constraints in remote sensing applications, using NASA’s Thunderhead Beowulf cluster for demonstration purposes throughout the chapter. Combined, the different topics covered by this chapter offer a thoughtful perspectiv

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Sergei A. Samoilenko,Marlene Laruelleor classification of hyperspectral imagery using neural networks are presented and discussed. Experimental results are provided from the viewpoint of both classification accuracy and parallel performance on a variety of parallel computing platforms, including two networks of workstations at Universi